College of Information Engineering, Zhejiang University of Technology, Hangzhou, China; Zhejiang Provincial Collaborative Innovation Center for High-end Digital Intelligence Diagnosis and Treatment Equipment, Hangzhou, China; Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou, China.
College of Information Engineering, Zhejiang University of Technology, Hangzhou, China; Zhejiang Provincial Collaborative Innovation Center for High-end Digital Intelligence Diagnosis and Treatment Equipment, Hangzhou, China; Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou, China.
Neuroimage. 2024 Sep;298:120766. doi: 10.1016/j.neuroimage.2024.120766. Epub 2024 Aug 12.
Streamline tractography locally traces peak directions extracted from fiber orientation distribution (FOD) functions, lacking global information about the trend of the whole fiber bundle. Therefore, it is prone to producing erroneous tracks while missing true positive connections. In this work, we propose a new bundle-specific tractography (BST) method based on a bundle-specific tractogram distribution (BTD) function, which directly reconstructs the fiber trajectory from the start region to the termination region by incorporating the global information in the fiber bundle mask. A unified framework for any higher-order streamline differential equation is presented to describe the fiber bundles with disjoint streamlines defined based on the diffusion vectorial field. At the global level, the tractography process is simplified as the estimation of BTD coefficients by minimizing the energy optimization model, and is used to characterize the relations between BTD and diffusion tensor vector under the prior guidance by introducing the tractogram bundle information to provide anatomic priors. Experiments are performed on simulated Hough, Sine, Circle data, ISMRM 2015 Tractography Challenge data, FiberCup data, and in vivo data from the Human Connectome Project (HCP) for qualitative and quantitative evaluation. Results demonstrate that our approach reconstructs complex fiber geometry more accurately. BTD reduces the error deviation and accumulation at the local level and shows better results in reconstructing long-range, twisting, and large fanning tracts.
束流追踪技术在局部追踪从纤维方向分布（FOD）函数中提取的峰值方向，缺乏整个纤维束趋势的全局信息。因此，在错过真正的阳性连接时，它容易产生错误的轨迹。在这项工作中，我们提出了一种新的基于束特定轨迹分布（BTD）函数的束特定追踪（BST）方法，该方法通过将全局信息纳入纤维束掩模，直接从起始区域重建纤维轨迹。提出了一种用于任何高阶流线微分方程的统一框架，以描述基于扩散向量场定义的不相交流线的纤维束。在全局水平上，追踪过程简化为通过最小化能量优化模型来估计 BTD 系数，并用于通过引入轨迹束信息来描述 BTD 和扩散张量向量之间的关系，以提供解剖学先验。在模拟的 Hough、Sine、Circle 数据、ISMRM 2015 追踪挑战赛数据、FiberCup 数据以及来自人类连接组计划（HCP）的体内数据上进行了实验，以进行定性和定量评估。结果表明，我们的方法可以更准确地重建复杂的纤维几何形状。BTD 减少了局部水平的误差偏差和累积，并在重建长程、扭曲和大扇区纤维束方面表现出更好的结果。
